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» Efficient Discovery of Confounders in Large Data Sets
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BMCBI
2010
153views more  BMCBI 2010»
13 years 10 months ago
Challenges in microarray class discovery: a comprehensive examination of normalization, gene selection and clustering
Background: Cluster analysis, and in particular hierarchical clustering, is widely used to extract information from gene expression data. The aim is to discover new classes, or su...
Eva Freyhult, Mattias Landfors, Jenny Önskog,...
PODS
2006
ACM
134views Database» more  PODS 2006»
14 years 10 months ago
Finding global icebergs over distributed data sets
Finding icebergs ? items whose frequency of occurrence is above a certain threshold ? is an important problem with a wide range of applications. Most of the existing work focuses ...
Qi Zhao, Mitsunori Ogihara, Haixun Wang, Jun Xu
EDBT
2008
ACM
111views Database» more  EDBT 2008»
14 years 10 months ago
On-line discovery of hot motion paths
We consider an environment of numerous moving objects, equipped with location-sensing devices and capable of communicating with a central coordinator. In this setting, we investig...
Dimitris Sacharidis, Kostas Patroumpas, Manolis Te...
JNW
2008
171views more  JNW 2008»
13 years 10 months ago
The Necessity of Semantic Technologies in Grid Discovery
Service discovery and its automation are some of the key features that a large scale, open distributed system must provide so that clients and users may take advantage of shared re...
Serena Pastore
JSAI
2001
Springer
14 years 2 months ago
Medical Knowledge Discovery on the Meningoencephalitis Diagnosis Studied by the Cascade Model
: The cascade model is a rule induction methodology that uses level-wise expansion of a lattice. An attribute-value pair is expressed as an item, and every node in the lattice is s...
Takashi Okada